#!/bin/bash

###############指定训练脚本执行路径###############
# cd到与test文件夹同层级目录下执行脚本，提高兼容性；test_path_dir为包含test文件夹的路径
cur_path=`pwd`
cur_path_last_dirname=${cur_path##*/}
if [ x"${cur_path_last_dirname}" == x"test" ];then
    test_path_dir=${cur_path}
    cd ..
    cur_path=`pwd`
else
    test_path_dir=${cur_path}/test
fi

#集合通信参数,不需要修改

export RANK_SIZE=1
export JOB_ID=10087
RANK_ID_START=0

# 数据集路径,保持为空,不需要修改
data_path=""

#基础参数，需要模型审视修改
#网络名称，同目录名称
Network="LSTM_ID0468_for_PyTorch"
#训练epoch
train_epochs=1
#训练batch_size
batch_size=128
#训练step
train_steps=1
#学习率
learning_rate=0.1

#维测参数，precision_mode需要模型审视修改
precision_mode="allow_fp32_to_fp16"
#维持参数，以下不需要修改
over_dump=False
data_dump_flag=False
data_dump_step="10"
profiling=False

# 帮助信息，不需要修改
if [[ $1 == --help || $1 == -h ]];then
    echo"usage:./train_performance_1P.sh <args>"
    echo " "
    echo "parameter explain:
    --precision_mode         precision mode(allow_fp32_to_fp16/force_fp16/must_keep_origin_dtype/allow_mix_precision)
    --over_dump		           if or not over detection, default is False
    --data_dump_flag		     data dump flag, default is False
    --data_dump_step		     data dump step, default is 10
    --profiling		           if or not profiling for performance debug, default is False
	--data_path		           source data of training
    -h/--help		             show help message
    "
    exit 1
fi

#参数校验，不需要修改
for para in $*
do
    if [[ $para == --precision_mode* ]];then
        precision_mode=`echo ${para#*=}`
    elif [[ $para == --over_dump* ]];then
        over_dump=`echo ${para#*=}`
        over_dump_path=${cur_path}/output/overflow_dump
        mkdir -p ${over_dump_path}
    elif [[ $para == --data_dump_flag* ]];then
        data_dump_flag=`echo ${para#*=}`
        data_dump_path=${cur_path}/output/data_dump
        mkdir -p ${data_dump_path}
    elif [[ $para == --data_dump_step* ]];then
        data_dump_step=`echo ${para#*=}`
    elif [[ $para == --profiling* ]];then
        profiling=`echo ${para#*=}`
        profiling_dump_path=${cur_path}/output/profiling
        mkdir -p ${profiling_dump_path}
    elif [[ $para == --data_path* ]];then
        data_path=`echo ${para#*=}`
    elif [[ $para == --batch_size* ]];then
        batch_size=`echo ${para#*=}`
    fi
done

PREC=""
if [[ $precision_mode == "amp" ]];then
    PREC="--apex"
fi
#校验是否传入data_path,不需要修改
if [[ $data_path == "" ]];then
    echo "[Error] para \"data_path\" must be confing"
    exit 1
fi

#进入训练脚本目录，需要模型审视修改
cd $cur_path/timit/

#训练前修改参数配置
sed -i "s|batch_size: 128|batch_size: ${batch_size}|g" conf/ctc_config.yaml
sed -i "s|num_epoches: 500|num_epoches: $train_epochs|g" conf/ctc_config.yaml
sed -i "s|data|${data_path}|g" conf/ctc_config.yaml
sed -i "s|data|${data_path}|g" ${data_path}/train/fbank.scp
sed -i "s|data|${data_path}|g" ${data_path}/dev/fbank.scp
sed -i "s|data|${data_path}|g" ${data_path}/test/fbank.scp


# 指定训练所使用的npu device卡id
device_id=0

# 校验是否指定了device_id,分动态分配device_id与手动指定device_id,此处不需要修改
if [ $ASCEND_DEVICE_ID ];then
    echo "device id is ${ASCEND_DEVICE_ID}"
elif [ ${device_id} ];then
    export ASCEND_DEVICE_ID=${device_id}
    echo "device id is ${ASCEND_DEVICE_ID}"
else
    "[Error] device id must be config"
    exit 1
fi

#非平台场景时source 环境变量
check_etp_flag=`env | grep etp_running_flag`
etp_flag=`echo ${check_etp_flag#*=}`
if [ x"${etp_flag}" != x"true" ];then
    source  ${test_path_dir}/env_npu.sh
fi

#训练开始时间，不需要修改
start_time=$(date +%s)

for((RANK_ID=$RANK_ID_START;RANK_ID<$((RANK_SIZE+RANK_ID_START));RANK_ID++));
do
    #设置环境变量，不需要修改
    echo "Device ID: $ASCEND_DEVICE_ID"
    export RANK_ID=$RANK_ID
    export PTCOPY_ENABLE=1
    export COMBINED_ENABLE=1
    #创建DeviceID输出目录，不需要修改
    if [ -d ${test_path_dir}/output/${ASCEND_DEVICE_ID} ];then
        rm -rf ${test_path_dir}/output/${ASCEND_DEVICE_ID}
        mkdir -p ${test_path_dir}/output/$ASCEND_DEVICE_ID/ckpt
    else
        mkdir -p ${test_path_dir}/output/$ASCEND_DEVICE_ID/ckpt
    fi
    
    #执行训练脚本，以下传参不需要修改，其他需要模型审视修改
    #--data_dir, --model_dir, --precision_mode, --over_dump, --over_dump_path，--data_dump_flag，--data_dump_step，--data_dump_path，--profiling，--profiling_dump_path
    nohup python3 -u steps/train_ctc.py \
    --device_id $ASCEND_DEVICE_ID \
    --apex \
    --loss_scale 128 \
    --opt_level O2 \
    --conf 'conf/ctc_config.yaml' > ${test_path_dir}/output/${ASCEND_DEVICE_ID}/train_${ASCEND_DEVICE_ID}.log 2>&1 &
done 
wait

#训练结束时间，不需要修改
end_time=$(date +%s)
e2e_time=$(( $end_time - $start_time ))

#训练完恢复参数配置
sed -i "s|batch_size: ${batch_size}|batch_size: 128|g" conf/ctc_config.yaml
sed -i "s|num_epoches: ${train_epochs}|num_epoches: 500|g" conf/ctc_config.yaml
sed -i "s|${data_path}|data|g" conf/ctc_config.yaml
sed -i "s|${data_path}|data|g" ${data_path}/train/fbank.scp
sed -i "s|${data_path}|data|g" ${data_path}/dev/fbank.scp
sed -i "s|${data_path}|data|g" ${data_path}/test/fbank.scp

#结果打印，不需要修改
echo "------------------ Final result ------------------"
#输出性能FPS，需要模型审视修改
step_time=`grep "Epoch =" $test_path_dir/output/${ASCEND_DEVICE_ID}/train_${ASCEND_DEVICE_ID}.log|awk 'END {print $9}'|sed 's/,$//'`
FPS=`echo|awk "{print 1/$step_time*$batch_size}"`
#打印，不需要修改
echo "Final Performance item/sec : $FPS"

#获取编译时间
CompileTime=`grep "Epoch =" $test_path_dir/output/${ASCEND_DEVICE_ID}/train_${ASCEND_DEVICE_ID}.log | head -2 | awk -F ' ' '{print $9}'|sed 's/,$//' | awk '{sum+=$1} END {print"",sum}' |sed s/[[:space:]]//g`

#输出训练精度,需要模型审视修改
train_accuracy=`grep "End training," ${test_path_dir}/output/${ASCEND_DEVICE_ID}/train_${ASCEND_DEVICE_ID}.log|awk 'END {print $10}'`
#打印，不需要修改
echo "Final Train Accuracy : ${train_accuracy}"
echo "E2E Training Duration sec : $e2e_time"

#性能看护结果汇总
#训练用例信息，不需要修改
BatchSize=${batch_size}
DeviceType=`uname -m`
CaseName=${Network}_bs${BatchSize}_${RANK_SIZE}'p'_'perf'

##获取性能数据，不需要修改
#吞吐量
ActualFPS=${FPS}
#单迭代训练时长
TrainingTime=`awk 'BEGIN{printf "%.2f\n",'${BatchSize}'*1000/'${FPS}'}'`

#从train_$ASCEND_DEVICE_ID.log提取Loss到train_${CaseName}_loss.txt中，需要根据模型审视
grep "cur_loss" $test_path_dir/output/${ASCEND_DEVICE_ID}/train_${ASCEND_DEVICE_ID}.log|awk -F 'total_loss = ' '{print $2}' | awk '{print $1}'|sed 's/,$//' >> $test_path_dir/output/$ASCEND_DEVICE_ID/train_${CaseName}_loss.txt

#最后一个迭代loss值，不需要修改
ActualLoss=`awk 'END {print}' $test_path_dir/output/$ASCEND_DEVICE_ID/train_${CaseName}_loss.txt`

#关键信息打印到${CaseName}.log中，不需要修改
echo "Network = ${Network}" > $test_path_dir/output/$ASCEND_DEVICE_ID/${CaseName}.log
echo "RankSize = ${RANK_SIZE}" >> $test_path_dir/output/$ASCEND_DEVICE_ID/${CaseName}.log
echo "BatchSize = ${BatchSize}" >> $test_path_dir/output/$ASCEND_DEVICE_ID/${CaseName}.log
echo "DeviceType = ${DeviceType}" >> $test_path_dir/output/$ASCEND_DEVICE_ID/${CaseName}.log
echo "CaseName = ${CaseName}" >> $test_path_dir/output/$ASCEND_DEVICE_ID/${CaseName}.log
echo "ActualFPS = ${ActualFPS}" >> $test_path_dir/output/$ASCEND_DEVICE_ID/${CaseName}.log
echo "TrainingTime = ${TrainingTime}" >> $test_path_dir/output/$ASCEND_DEVICE_ID/${CaseName}.log
echo "ActualLoss = ${ActualLoss}" >> $test_path_dir/output/$ASCEND_DEVICE_ID/${CaseName}.log
echo "E2ETrainingTime = ${e2e_time}" >> $test_path_dir/output/$ASCEND_DEVICE_ID/${CaseName}.log
echo "CompileTime = ${CompileTime}" >> $test_path_dir/output/$ASCEND_DEVICE_ID/${CaseName}.log